healthcare benchmark
HealthBench Hard is a challenging variation of HealthBench that evaluates large language models in healthcare using 5,000 multi-turn conversations and evaluation criteria validated by 262 physicians from 60 countries.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelME | Score42.80% | Percentile100.00% | Participants9 | EvidenceC | Evaluated |
| Rank02 | ModelOP | Score33.10% | Percentile87.50% | Participants9 | EvidenceC | Evaluated |
| Rank03 | ModelOP | Score32.70% | Percentile75.00% | Participants9 | EvidenceC | Evaluated |
| Rank04 | ModelOP | Score32.00% | Percentile62.50% | Participants9 | EvidenceC | Evaluated |
| Rank05 | ModelOP | Score30.00% | Percentile50.00% | Participants9 | EvidenceC | Evaluated |
| Rank06 | ModelOP | Score25.90% | Percentile37.50% | Participants9 | EvidenceC | Evaluated |
| Rank07 | ModelOP | Score22.90% | Percentile25.00% | Participants9 | EvidenceC | Evaluated |
| Rank08 | ModelOP | Score10.80% | Percentile12.50% | Participants9 | EvidenceC | Evaluated |
| Rank09 | ModelOP | Score1.60% | Percentile0.00% | Participants9 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What HealthBench Hard measures and how its scores work.
HealthBench Hard is a healthcare benchmark for evaluating large language models through 5,000 multi-turn conversations.
It measures large language models' performance and safety in healthcare using a Score reported as a ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of C.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about HealthBench Hard.
Muse Spark is currently ranked first with 42.80%.
The current leaders are Muse Spark (42.80%), GPT-5.6 Sol (33.10%), and GPT-5.6 Terra (32.70%).
GPT-5.6 Luna has the lowest matched official input price at $0.20 input / $1.2 output per 1M tokens.
The fastest matched records are GPT OSS 20B (1,000.00 tok/s via Groq), GPT OSS 120B (500.00 tok/s via Groq), and GPT-5.5 Instant (206.89 tok/s via OpenAI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures large language models' performance and safety in healthcare using a Score reported as a ratio.
Yes. Higher values rank better for this benchmark.
9 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis healthbench hard AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Muse Spark currently leads HealthBench Hard with 42.80%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.